A Framework for Maternal Physical Activities and Health Monitoring Using Wearable Sensors

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 15; p. 4949
• Main Authors: Ullah, Farman, Iqbal, Asif, Iqbal, Sumbul, Kwak, Daehan, Anwar, Hafeez, Khan, Ajmal, Ullah, Rehmat, Siddique, Huma, Kwak, Kyung-Sup
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 21.07.2021; MDPI
• Subjects: Accelerometers; Accuracy; Algorithms; BLE; Datasets; Exercise; Feedback; human-centric computing; Literature reviews; Machine learning; maternal physical activity recognition; Preeclampsia; Pregnancy; raspberry-PI; Sensors; Smartphones; wearable sensors
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21154949

We propose a physical activity recognition and monitoring framework based on wearable sensors during maternity. A physical activity can either create or prevent health issues during a given stage of pregnancy depending on its intensity. Thus, it becomes very important to provide continuous feedback by recognizing a physical activity and its intensity. However, such continuous monitoring is very challenging during the whole period of maternity. In addition, maintaining a record of each physical activity, and the time for which it was performed, is also a non-trivial task. We aim at such problems by first recognizing a physical activity via the data of wearable sensors that are put on various parts of body. We avoid the use of smartphones for such task due to the inconvenience caused by wearing it for activities such as “eating”. In our proposed framework, a module worn on body consists of three sensors: a 3-axis accelerometer, 3-axis gyroscope, and temperature sensor. The time-series data from these sensors are sent to a Raspberry-PI via Bluetooth Low Energy (BLE). Various statistical measures (features) of this data are then calculated and represented in features vectors. These feature vectors are then used to train a supervised machine learning algorithm called classifier for the recognition of physical activity from the sensors data. Based on such recognition, the proposed framework sends a message to the care-taker in case of unfavorable situation. We evaluated a number of well-known classifiers on various features developed from overlapped and non-overlapped window size of time-series data. Our novel dataset consists of 10 physical activities performed by 61 subjects at various stages of maternity. On the current dataset, we achieve the highest recognition rate of 89% which is encouraging for a monitoring and feedback system.